Vehicle human machine interface generating system and method for generating the same

ABSTRACT

A method for generating a vehicle human machine interface is disclosed. The identities of each of a plurality of occupants in a vehicle are determined. A plurality of interface settings corresponding to the plurality of occupants are obtained according to the identities. A machine learning operation is performed according to the identities of the plurality of occupants and the plurality of interface settings. The vehicle human machine interface is generated according to a result of the machine learning operation.

BACKGROUND

The present disclosure relates to a vehicle human machine interface(HMI) generating system and a method for generating a vehicle HMI.

An HMI is a medium of interaction and information exchange between asystem and its users. In the vehicle industry, the HMI may realize theconversion between the internal information of the vehicle and a formacceptable to human beings. The HMI may be a software application or auser interface designed to interact between the user and the vehicle,for example.

In some environments, the occupants, including the driver and thepassengers, may make manual adjustments to customize the HMI environmentto align with their preferences. The present disclosure provides animproved vehicle HMI generating system and a method for generating avehicle HMI.

SUMMARY

According to one aspect of the present disclosure, a method forgenerating a vehicle human machine interface (HMI) is disclosed. Themethod may include determining the identities of each of a plurality ofoccupants in a vehicle. In addition, the method may include obtaining aplurality of interface settings corresponding to the plurality ofoccupants according to the identities. The method may also includemeshing the identities of the plurality of occupants and the pluralityof interface settings. The method may include generating the vehicle HMIaccording to the meshing.

According to another aspect of the present disclosure, a vehicle HMIgenerating system is disclosed. The system may include a database, anoccupation determination device, a biometric identification device, anda computation device. The database may store a plurality of interfacesettings corresponding to a plurality of occupants. The occupationdetermination device may be used to determine an occupation status in avehicle. The biometric identification device may be used to determine anidentity information corresponding to more than one occupant in thevehicle based on the occupation status. The computation device mayperform a machine learning operation to generate a vehicle HMI based onthe plurality of interface settings corresponding to the more than oneoccupants and the occupation status corresponding to the more than oneoccupants.

According to still another aspect of the present disclosure, anon-transitory computer-readable medium having instructions storedthereon is disclosed. When executed by at least one processor, thenon-transitory computer-readable medium causes the at least oneprocessor to perform a method for generating a vehicle HMI. The methodmay determine the identities of each of a plurality of occupants in avehicle. In addition, the method may obtain a plurality of interfacesettings corresponding to the plurality of occupants according to theidentities. The method may also perform a machine learning operationaccording to the identities of the plurality of occupants and theplurality of interface settings. The vehicle HMI may be generatedaccording to a result of the machine learning operation.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated herein and form a partof the specification, illustrate implementations of the presentdisclosure and, together with the description, further serve to explainthe present disclosure and to enable a person skilled in the pertinentart to make and use the present disclosure.

FIG. 1 illustrates a schematic diagram of the interaction between thevehicle and the users according to some embodiments of the presentdisclosure.

FIGS. 2A-2C illustrate schematic diagrams of an exemplary human machineinterface (HMI) between the vehicle and the users according to someembodiments of the present disclosure.

FIG. 3 illustrates a schematic diagram of another exemplary HMI betweenthe vehicle and the users according to some embodiments of the presentdisclosure.

FIG. 4 illustrates a schematic diagram of yet another exemplary HMIbetween the vehicle and the users according to some embodiments of thepresent disclosure.

FIG. 5 illustrates a schematic diagram of still another exemplary HMIbetween the vehicle and the users according to some embodiments of thepresent disclosure.

FIG. 6 illustrates a flowchart of an exemplary method for generating theHMI according to some embodiments of the present disclosure.

FIG. 7 illustrates an exemplary HMI generating system according to someembodiments of the present disclosure.

Implementations of the present disclosure will be described withreference to the accompanying drawings.

DETAILED DESCRIPTION

Although specific configurations and arrangements are discussed, itshould be understood that this is done for illustrative purposes only.As such, other configurations and arrangements may be used withoutdeparting from the scope of the present disclosure. Also, the presentdisclosure may also be employed in a variety of other applications.Functional and structural features as described in the presentdisclosures may be combined, adjusted, and modified with one another andin ways not specifically depicted in the drawings, such that thesecombinations, adjustments, and modifications are within the scope of thepresent discloses.

In general, terminology may be understood at least in part from usage incontext. For example, the term “one or more” as used herein, dependingat least in part upon context, may be used to describe any feature,structure, or characteristic in a singular sense or may be used todescribe combinations of features, structures or characteristics in aplural sense. Similarly, terms, such as “a,” “an,” or “the,” again, maybe understood to convey a singular usage or to convey a plural usage,depending at least in part upon context. In addition, the term “basedon” may be understood as not necessarily intended to convey an exclusiveset of factors and may, instead, allow for existence of additionalfactors not necessarily expressly described, again, depending at leastin part on context.

It should be readily understood that the meaning of “on,” “above,” and“over” in the present disclosure should be interpreted in the broadestmanner such that “on” not only means “directly on” something but alsoincludes the meaning of “on” something with an intermediate feature or alayer therebetween, and that “above” or “over” not only means themeaning of “above” or “over” something but may also include the meaningit is “above” or “over” something with no intermediate feature or layertherebetween (i.e., directly on something).

Further, spatially relative terms, such as “beneath,” “below,” “lower,”“above,” “upper,” and the like, may be used herein for ease ofdescription to describe one element or feature's relationship to anotherelement(s) or feature(s) as illustrated in the figures. The spatiallyrelative terms are intended to encompass different orientations of thedevice in use or operation in addition to the orientation depicted inthe figures. The apparatus may be otherwise oriented (rotated 90 degreesor at other orientations) and the spatially relative descriptors usedherein may likewise be interpreted accordingly.

FIG. 1 illustrates a schematic diagram of an interaction between thevehicle 100 and the users 102, 104, and 106 according to someembodiments of the present disclosure while FIGS. 2A-2C illustrateschematic diagrams of exemplary HMIs 202, 204, and 206 between thevehicle 100 and the users 102, 104, and 106 according to someembodiments of the present disclosure.

The human machine interface (HMI) may allow a person to connect to orinteract with a machine, system, or device. When a driver, such as theuser 102 in FIGS. 1 and 2A, sits in the driver's seat of the vehicle100, the HMI 202 may be used to communicate with the vehicle 100. Insome implementations, the HMI 202 may include buttons and/or the screenson a dashboard, steering wheel, and/or instrument panels. In otherimplementations, the HMI 202 may include voice control, voiceidentification, camera, haptic feedback, gesture identification andcontrol, virtual assistant, and other suitable applications. In someimplementations, the HMI may apply to infotainment screens, touchpads,navigation buttons, or even simple single-function controls. The HMI mayor may not be tangible. Audible and actionable results may also beincluded in the HMI.

In some implementations, every user may have a personalized orcustomized HMI. For example, the user 102 may communicate with thevehicle 100 through the HMI 202 which is customized for the user 102.The user 104 may communicate with the vehicle 100 through the HMI 204which may be customized for the user 104. The user 106 may communicatewith the vehicle 100 through the HMI 206 which is customized for theuser 106. In some implementations, the HMIs 202, 204, and 206 may bepredefined and stored in the physical storage device in the vehicle 100.When the user 102, 104, or 106 enters the vehicle 100, the vehicle 100may identify the user and provide the customized HMI corresponding tothe user. In some implementations, the settings of the HMIs 202, 204,and 206 may be stored in a cloud storage space and downloaded to thevehicle 100 through suitable ways, such as wireless communications, whenthe vehicle 100 identifies the user.

In some implementations, the HMI 202 corresponding to the user 102 mayinclude vehicle applications, such as an entertainment application, seatadjustment setting, music playlist, temperature setting, and so on. TheHMI 204 corresponding to the user 104 or the HMI 206 corresponding tothe user 106, may have different content with the HMI 202, or may havepartially similar content as the HMI 202 for the user 102, which isdescribed as a non-limiting example. In some implementations, the sameuser, such as the user 102, may have more than one HMI setting, and thevehicle 100 may identify the position of the user 102, like a driver orpassenger, and provide different HMI corresponding to the position ofthe user 102.

FIG. 2B illustrates a schematic diagram of the machine learningoperations meshing the HMIs 202, 204, and 206 to generate a new HMI 210.In some implementations, the HMIs 202, 204, and 206 may be the interfacebetween the users 102, 104, and 106 and the vehicle 100. In someimplementations, as shown in FIGS. 2A-2B, the HMI 202 may display afirst application (App 1), a second application (App 2), and a radioplayer (Radio A); the HMI 204 may display a third application (App 3), afourth application (App 4), and a temperature control screen (Temp C);and the HMI 206 may display a fifth application (App 5), a music player(Music Player C), and a navigator (Map C). When a new user, for examplea new passenger, enters the vehicle 100, the machine learning operationsmay be performed to generate the HMI 210.

During the machine learning operations, the applications, including App1, App 2, App 3, App4, and App 5, may be collected and shown in acomplete interface 208. Next, in some implementations, the user maychoose which application would be suitable for the current scenario andthen obtain the HMI 210. In some implementations, the applications maybe chosen automatically based on the users' historical record. In someimplementations, the locations of each application displayed on theinterface may be decided by the user. In some implementations, thelocations of each application displayed on the interface may beautomatically decided during the machine learning operations.

In some implementations, the machine learning operations may learn oridentify the amount and/or identities of the users, obtain theinformation of the available screen space for the interface, anddetermine the applications fit the users and/or the current scenario, togenerate the HMI 210. In some implementations, the synergy between theapplications may be determined during the machine learning operations.For example, when some applications are social networking applications,or are media streaming applications, the synergy may be determinedduring the machine learning operations, and then the commonality ofthese applications will be considered when generating the HMI 210.

In some implementations, the location and/or the interface size of theselected applications may be further adjusted to fit the screendisplaying the HMI 210. For example, as shown in FIG. 2B, theapplication size of App 3 may be enlarged, and the locations of App 1and App 3 may be moved to fit the HMI 210. In some implementations, themachine learning operations may determine the best place, size, and/orshape of the applications in the HMI 210.

FIG. 2C illustrates another schematic diagram of the machine learningoperations meshing the HMIs 202, 204, and 206 to generate a new HMI 210.In some implementations, the users' preferred settings or preferences ofHMIs 202, 204, and 206 may be defined and stored in advance. When users102, 104, and/or 106 enter the vehicle 100 together or sequentially, theidentities and/or positions in the vehicle 100 of users 102, 104, and/or106 may be determined first, and the determined users' preferredsettings or preferences may be used for generating the HMI 214. In someimplementations, the machine learning operations may obtain portionsthat were agreed by the users or have common preferences in HMIs 202,204, and 206 corresponding to the users 102. 104, and 106, and theagreed or common portions may be given a higher priority during themachine learning operations. For example, as shown in FIG. 2C, theagreed or common portions may include the navigator, the music player,and/or the application, and may be shown in the interface 212. Themachine learning operations may further adjust the size and/or thelocations of the navigator, the music player, and/or the application togenerate the HMI 214 fitting the screen size of the HMI 214. In someimplementations, the machine learning operations may determine the bestplace, size, and/or shape of the navigator, the music player, and/or theapplication in the HMI 214.

In some implementations, when a separate display is designated to thedriver, the machine learning operations may further generate a differentHMI for the driver. In some implementations when a separate display isdesignated to the backseat passengers, the machine learning operationsmay further generate a different HMI for the backseat passengers.

FIG. 3 illustrates a schematic diagram of another exemplary HMI 302between the vehicle 100 and the users 102 and 104 according to someembodiments of the present disclosure. In some implementations, multipleoccupants, such as the user 102 and the user 104, may be in the samevehicle, and the HMI 302 may be provided based on this scenario. In someimplementations, the user 102 may have a favorite or predefined HMI 202and the user 104 may have a favorite or predefined HMI 204. When theuser 102 and the user 104 enter the vehicle 100 together, the user 102enters the vehicle 100 after the user 104, or the user 104 enters thevehicle 100 after the user 102, the HMI in the vehicle 100 may bechanged when detecting the change of the occupants, including the driverand/or the passengers. The new HMI 302 may be provided by merging theHMI 202 and the HMI 204. In some implementations, an HMI generatingsystem may be applied to provide the HMI 302.

In some implementations, the HMI generating system may perform anidentification operation first to determine the identities of theoccupants in the vehicle 100 and/or the seating of the occupants in thevehicle 100. The HMI generating system may obtain the predefined HMIs202 and 204 and/or the historical record of HMI used by each identifieduser 102 and 104, and perform a computing based on the predefined HMIs202 and 204 and/or the historical record to obtain a new HMI 302. Insome implementations, the predefined HMIs 202 and 204 and/or thehistorical record may be stored in the vehicle 100, such as anon-volatile storage device in the vehicle 100, or be stored in a cloudspace. In some implementations, the computation of merging the HMIs 202and 204 may be performed by the machine learning operations or neuralnetwork systems.

In some implementations, the machine learning operations may obtain thepredefined HMIs 202 and 204 and/or the historical record, and dividethat information into multiple calculation nodes and interlayers toperform the computation. For example, the entertainment application, theseat adjustment setting, the music playlist, or the temperature settingcorresponding to the user 102 stored in the HMI 202 or the historicalrecord of the user 102 may be divided into multiple calculation nodes.Similarly, the entertainment application, the seat adjustment setting,the music playlist, or the temperature setting corresponding to the user104 stored in the HMI 204 or the historical record of the user 104 maybe divided into multiple calculation nodes. In some implementations,some weighting factors may be chosen to perform the computation. Forexample, the seating of the occupants, the role of the occupants, suchas a driver or a passenger, or the relationship between the occupants,may be chosen as one of the weighting factors in the computation. Insome implementations, the usage history of the HMIs in the historicalrecord may be also chosen as one of the weighting factors in thecomputation, such as the using frequency or the using time.

In some implementations, the machine learning operations may include thesupervised learning algorithms that contain a user's input and themathematical models to estimate and generate the new HMI 302. In someimplementations, the users may provide some predefined trainingscenarios as the training data of the machine learning operations. Insome implementations, the machine learning operations may include theunsupervised learning algorithms that take data from the calculationnodes of the HMI 202 and/or the HMI 204, and find structures in thesecalculation nodes, like grouping or clustering of data points. Forexample, in some implementations, one or more convolutional neuralnetwork (CNN) may be used to classify the divided calculation node andthe interlayers obtained from the HMI 202, the HMI 204, and thehistorical record, and then one or more artificial neural networks (ANN)may be used to estimate and generate the new HMI 302. As shown in FIG. 3, based on different weighting factors, the same input source, such asthe HMIs 202 and 204, may have multiple different outputs, such as thenew HMIs 304 and 306, that may be used in the vehicle 100.

FIG. 4 illustrates a schematic diagram of another exemplary HMI 402between the vehicle 100 and the users 102 and 104 according to someembodiments of the present disclosure. In some implementations, themachine learning operations may take into account of the roles of theoccupants into consideration. For example, in the situation that theuser 102 and the user 104 are in the vehicle 100 together, when the user102 is the driver, the machine learning operations may consider the roleof the user 102 and add weight to some applications required fordriving. For example, the navigation applications, in the HMI 202corresponding to the user 102, may be used to generate the HMI 402 whenthe user 102 is detected as the driver of the vehicle 100. In anotherexample, when the user 102 is the backseat passenger, the machinelearning operations may add weight to entertainment applications, suchas video games, in the HMI 202 corresponding to the user 102 whengenerating the HMI 404.

In some implementations, the HMIs 302, 304, 306, 402, or 404 may be amerge or an integration of the HMI 202 and the HMI 204. For example, theHMIs 302, 304, 306, 402, or 404 may show the complete content of boththe HMI 202 and the HMI 204, or show a portion of content of the HMI 202and a portion of content of the HMI 204. In some implementations, theHMIs 302, 304, 306, 402, or 404 may be a new HMI providing an interlockof the HMI 202 and the HMI 204. For example, the machine learningoperations may obtain a relationship of the settings, the contents, orthe applications between the HMI 202 and the HMI 204 individuallycorresponding to the user 102 and the user 104, and the HMIs 302, 304,306, 402, or 404 may function based on the learned relationship.

In some implementations, the identities of the users may be determinedby the users' biometric characteristics. For example, the vehicle 100may be equipped with one or more biometric identification devices, suchas a fingerprint scanner, voice recognition device, facial recognitiondevice, iris identification device, heart-rate sensor, and so on. Thepredefined HMI corresponds to each individual user and may be stored inthe vehicle 100. When a user enters the vehicle 100, the vehicle mayidentify the user, such as identifying the user 102 located on the leftrear passenger seat, and then provide the corresponding HMI 202 to theHMI generating system as an information source for machine learningoperations.

FIG. 5 illustrates a schematic diagram of another exemplary HMI 502between the vehicle 100 and the users 102, 104, and 106 according tosome embodiments of the present disclosure. In some implementations, theusers in the vehicle 100 may have some predefined relationship, such asa family including parents and child. For example, as shown in FIG. 5 ,the user 102 and the user 104 may be the parents, and the user 106 maybe a child. When the users enter the vehicle 100, the vehicle 100 mayfirst determine the identities of each user, and after obtaining theidentities of the users and compare with the predefined relationship,the vehicle may determine the users 102, 104, and 106 are parents andchildren. In this scenario, the HMI generating system may estimate andgenerate an HMI 502 suitable for a family group. In an example, otherkinds of relationship between multiple users may be also predefined,such as friends, carpooler, or couples, and the HMI generating systemmay estimate and generate different HMIs 504 or 506 accordingly. In someimplementations, the relationship may be a weighting factor in themachine learning operations, and the HMI may be estimated and generatedaccordingly.

For another example, in some implementations, the group of users 102,104, and 106 may be in a carpool relationship, and the HMI generatingsystem may estimate the preference of each occupant or some commonpreferences of all the occupants to generate the HMI 502. For example,the users 102, 104, and 106 may have different music playlists ordifferent favorite podcasts, the HMI generating system may perform themachine learning operations by using the predefined informationcorresponding to individual users as the learning resource and generatethe HMI 502 to provide some common favorite playlists for all occupantsin this group.

In some implementations, the vehicle 100 may have multiple outputdevices, like multiple monitors or multiple speakers. The HMI 504 may beprovided to the driver and another HMI 506 may be provided to thepassengers that may avoid distracting the driver. For example, the HMIgenerating system may determine the identities and the seating positionsof the driver and the passengers and obtain their predefined preferencesto generate one or more than one HMI, and the HMI 504 may be provided tothe driver for the navigation or driving safety instructions, and theHMI 506 may be provided to the child for the entertainment programs.

FIG. 6 illustrates a flowchart of an exemplary method 600 for generatingthe HMI according to some embodiments of the present disclosure whileFIG. 7 illustrates an exemplary HMI generating system 700 according tosome embodiments of the present disclosure. For the purpose of betterdescribing the present disclosure, the method 600 in FIG. 7 and the HMIgenerating system 700 in FIG. 7 will be discussed together. It isunderstood that the operations shown in method 600 are not exhaustiveand that other operations may be performed as well before, after, orbetween any of the illustrated operations. Further, some of theoperations may be performed simultaneously, or in a different order thanshown in FIG. 6 and FIG. 7 .

The HMI generating system 700 may include a database 702, an occupationdetermination device 704, a biometric identification device 706, and acomputation device 708. The database 702 may store a plurality ofinterface settings corresponding to a plurality of occupants. Theoccupation determination device 704 may be used to determine anoccupation status in a vehicle. The biometric identification device 706may be used to determine an identity information corresponding to morethan one occupant in the vehicle based on the occupation status. Thecomputation device 708 may perform a machine learning operation togenerate a vehicle HMI based on the plurality of interface settingscorresponding to the more than one occupant and the occupation statuscorresponding to the more than one occupant.

As shown in the operation 602 of FIG. 6 , the identities of each of aplurality of occupants in a vehicle are determined. For example, thebiometric identification device 706 may be used to determine theoccupants in the vehicle 100. In some implementations, the biometricidentification device 706 may be used to determine the users' biometriccharacteristics. For example, the vehicle 100 may be equipped thebiometric identification device 706, and the biometric identificationdevice 706 may include the fingerprint scanner, the voice recognitiondevice, the facial recognition device, the iris identification device,the heart-rate sensor, and so on. When a new user, such as a driver or apassenger, enters the vehicle 100, the biometric identification device706 may identify the user's identity and perform the followingoperations according to the user's identity.

As shown in the operation 604 of FIG. 6 , a plurality of interfacesettings corresponding to the plurality of occupants are obtainedaccording to the identities. In some implementations, the interfacesettings corresponding to the plurality of occupants may be predefinedand stored in the database 702. The interface settings may includemultiple HMIs corresponding to each user (occupant in the vehicle 100).The interface settings may further include the historical record of theusage history corresponding to each user. After the identities of eachoccupant in the vehicle 100 are determined, the interface settingscorresponding to the identified occupants, including the predefined HMIsand the historical record, may be loaded to the HMI generating system700 for the following operations. In some implementations, theoccupation determination device 704 may include an occupant positionsensor for determining a seating information of each of the more thanone occupant in the vehicle. The seating information indicated aposition of each of the more than one occupant in the vehicle.

As shown in the operation 606 of FIG. 6 , a machine learning operationis performed according to the identities of the plurality of occupantsand the plurality of interface settings. In some implementations, thecomputation device 708 is used to perform the machine learningoperation. In some implementations, the computation device 708 mayinclude a processor. The processor may be used to perform the machinelearning operation according to the seating information, the identities,and the interface settings corresponding to the identified occupants inthe vehicle 100. As shown in the operation 608 of FIG. 6 , the vehicleHMI is generated according to a result of the machine learningoperation.

According to one aspect of the present disclosure, a method forgenerating a vehicle HMI is disclosed. The identities of each of aplurality of occupants in a vehicle are determined. A plurality ofinterface settings corresponding to the plurality of occupants areobtained according to the identities. A machine learning operation isperformed according to the identities of the plurality of occupants andthe plurality of interface settings. The vehicle HMI is generatedaccording to a result of the machine learning operation.

In some implementations, when a new occupant enters the vehicle, anidentity of the new occupant is determined. In some implementations, abiometric identification device is provided in the vehicle, and theidentities of the plurality of occupants are determined through thebiometric identification device.

In some implementations, a seating information of the plurality ofoccupants is determined, and a weighting operation is performed based onthe seating information when performing the machine learning operation.

In some implementations, the plurality of interface settings arecompared with a historical record of the interface setting correspondingto each occupant, and the machine learning operation is performedaccording to the historical record, the identities of the plurality ofoccupants, and the plurality of interface settings.

In some implementations, the plurality of interface settings are mergedby choosing a portion of the interface setting from a complete interfacesetting corresponding to each occupant, and the vehicle HMI is generatedaccording to the portion of the interface setting corresponding to eachoccupant. In some implementations, the plurality of interface settingscorrespond to the plurality of occupants are interlocked. In someimplementations, the vehicle HMI is generated according to a predefinedrelationship of the plurality of occupants.

In some implementations, a common portion in the plurality of interfacesettings corresponding to the plurality of occupants is determined, andthe vehicle HMI is generated according to the common portion.

In some implementations, a preference in the plurality of interfacesettings corresponding to the plurality of occupants is extracted, andthe preference is merged according to the identities of the plurality ofoccupants to generate the vehicle HMI.

In some implementations, an identity information of each of theplurality of occupants is collected, and the historical record of theinterface setting corresponding to the identity information of each ofthe plurality of occupants is stored.

In some implementations, the historical record, the identities of theplurality of occupants, the seating information, and the plurality ofinterface settings corresponding to the plurality of occupants aredivided into a plurality of calculation nodes, a connection between theplurality of calculation nodes is estimated, and the vehicle HMI isgenerated according to the connection and the plurality of calculationnodes.

According to another aspect of the present disclosure, a vehicle HMIgenerating system is disclosed. The system includes a database, anoccupation determination device, a biometric identification device, anda computation device. The database is stored a plurality of interfacesettings corresponding to a plurality of occupants. The occupationdetermination device is used to determine an occupation status in avehicle. The biometric identification device is used to determine anidentity information corresponding to more than one occupants in thevehicle based on the occupation status. The computation device performsa machine learning operation to generate a vehicle HMI based on theplurality of interface settings corresponding to the more than oneoccupants and the occupation status corresponding to the more than oneoccupants.

In some implementations, the biometric identification device includes atleast one of a fingerprint scanner, a voice recognition device, a facialrecognition device, an iris identification device, and a heart-ratesensor.

In some implementations, the plurality of interface settings include aplurality of predefined HMIs corresponding to each of the plurality ofoccupants and a plurality of historical records corresponding to each ofthe plurality of occupants.

In some implementations, the occupation determination device includes anoccupant position sensor determining a seating information of each ofthe more than one occupant in the vehicle. The seating informationindicates a position of each of the more than one occupant in thevehicle.

In some implementations, the computation device includes a processorconfigured to perform a method for generating the vehicle HMI. Themethod includes performing the machine learning operation according tothe seating information of each of the more than one occupant in thevehicle, the plurality of predefined HMIs corresponding to each of themore than one occupants in the vehicle, and the plurality of historicalrecords corresponding to each of the more than one occupants in thevehicle.

In some implementations, the processor divides the plurality ofhistorical records, the plurality of predefined HMIs, and the seatinginformation corresponding to each of the more than one occupant into aplurality of calculation nodes, estimates a connection between theplurality of calculation nodes, and generates the vehicle HMI based onto the connection and the plurality of calculation nodes.

In some implementations, the processor merges the plurality ofpredefined HMIs by choosing a portion of the plurality of interfacesettings corresponding to the more than one occupant in the vehicle, andgenerates the vehicle HMI according to the chosen portion of theplurality of interface settings.

In some implementations, the processor interlocks the plurality ofpredefined HMIs by determining a connection between the plurality ofinterface settings corresponding to the more than one occupant in thevehicle, and generates the vehicle HMI according to the connectionbetween the plurality of interface settings.

In some implementations, the processor determines a relationship betweenthe more than one occupant in the vehicle, and generates the vehicle HMIaccording to relationship between the more than one occupants.

According to still another aspect of the present disclosure, anon-transitory computer-readable medium having instructions storedthereon is disclosed. When executed by at least one processor, thenon-transitory computer-readable medium causes the at least oneprocessor to perform a method for generating a vehicle HMI. Theidentities of each of a plurality of occupants in a vehicle aredetermined. A plurality of interface settings corresponding to theplurality of occupants are obtained according to the identities. Amachine learning operation is performed according to the identities ofthe plurality of occupants and the plurality of interface settings. Thevehicle HMI is generated according to a result of the machine learningoperation.

The foregoing description of the specific implementations may be readilymodified and/or adapted for various applications. Therefore, suchadaptations and modifications are intended to be within the meaning andrange of equivalents of the disclosed implementations, based on theteaching and guidance presented herein.

The breadth and scope of the present disclosure should not be limited byany of the above-described exemplary implementations, but should bedefined only in accordance with the following claims and theirequivalents.

What is claimed is:
 1. A method for generating a vehicle human machineinterface, comprising: determining identities of each of a plurality ofoccupants in a vehicle; obtaining a plurality of interface settingscorresponding to the plurality of occupants in the vehicle according tothe identities; and meshing the plurality of interface settings fordisplay on the vehicle human machine interface.
 2. The method of claim1, wherein determining identities of each of the plurality of occupantsin the vehicle comprises determining an identity of a new occupant whenthe new occupant enters the vehicle.
 3. The method of claim 1, furthercomprising: providing a biometric identification device in the vehicle;and determining identities of the plurality of occupants through thebiometric identification device.
 4. The method of claim 1, furthercomprising: determining a seating information of the plurality ofoccupants; and performing a weighting operation based on the seatinginformation when meshing the plurality of interface settings.
 5. Themethod of claim 4, wherein meshing the plurality of interface settings,comprises: comparing the plurality of interface settings with ahistorical record of the interface setting corresponding to eachoccupant; and meshing the plurality of interface settings according tothe historical record, the identities of the plurality of occupants, andthe plurality of interface settings.
 6. The method of claim 5, whereinmeshing the plurality of interface settings for display on the vehiclehuman machine interface, comprises at least one of: merging theplurality of interface settings by choosing a portion of the interfacesetting from a complete interface setting corresponding to eachoccupant, and generating the vehicle human machine interface accordingto the portion of the interface setting corresponding to each occupant;interlocking the plurality of interface settings correspond to theplurality of occupants; and generating the vehicle human machineinterface according to a predefined relationship of the plurality ofoccupants.
 7. The method of claim 6, wherein interlocking the pluralityof interface settings correspond to the plurality of occupants,comprises: determining a common portion in the plurality of interfacesettings corresponding to the plurality of occupants; and generating thevehicle human machine interface according to the common portion.
 8. Themethod of claim 5, wherein meshing the plurality of interface settingsaccording to the identities of the plurality of occupants and theplurality of interface settings, comprises: extracting a preference inthe plurality of interface settings corresponding to the plurality ofoccupants; and merging the preference according to the identities of theplurality of occupants to generate the vehicle human machine interface.9. The method of claim 5, further comprising: collecting an identityinformation of each of the plurality of occupants; and storing thehistorical record of the interface setting corresponding to the identityinformation of each of the plurality of occupants.
 10. The method ofclaim 5, wherein meshing the plurality of interface settings comprises:dividing the historical record, the identities of the plurality ofoccupants, the seating information, and the plurality of interfacesettings corresponding to the plurality of occupants into a plurality ofcalculation nodes; estimating a connection between the plurality ofcalculation nodes; and generating the vehicle human machine interfaceaccording to the connection and the plurality of calculation nodes. 11.A vehicle human machine interface generating system, comprising: adatabase storing a plurality of interface settings corresponding to aplurality of occupants; an occupation determination device determiningan occupation status in a vehicle; a biometric identification devicedetermining an identity information corresponding to more than oneoccupant in the vehicle based on the occupation status; a computationdevice performing a machine learning operation to generate a vehiclehuman machine interface based on the plurality of interface settingscorresponding to the more than one occupant and the occupation statuscorresponding to the more than one occupant.
 12. The vehicle humanmachine interface generating system of claim 11, wherein the biometricidentification device comprises at least one of a fingerprint scanner, avoice recognition device, a facial recognition device, an irisidentification device, and a heart-rate sensor.
 13. The vehicle humanmachine interface generating system of claim 11, wherein the pluralityof interface settings comprise a plurality of predefined human machineinterfaces corresponding to each of the plurality of occupants and aplurality of historical records corresponding to each of the pluralityof occupants.
 14. The vehicle human machine interface generating systemof claim 11, wherein the occupation determination device comprises anoccupant position sensor determining a seating information of each ofthe more than one occupant in the vehicle, wherein the seatinginformation indicates a position of each of the more than one occupantin the vehicle.
 15. The vehicle human machine interface generatingsystem of claim 14, wherein the computation device comprises: aprocessor configured to perform a method for generating the vehiclehuman machine interface, comprising: performing the machine learningoperation according to the seating information of each of the more thanone occupant in the vehicle, the plurality of predefined human machineinterfaces corresponding to each of the more than one occupants in thevehicle, and the plurality of historical records corresponding to eachof the more than one occupants in the vehicle.
 16. The vehicle humanmachine interface generating system of claim 15, wherein the processordivides the plurality of historical records, the plurality of predefinedhuman machine interfaces, and the seating information corresponding toeach of the more than one occupant into a plurality of calculationnodes, estimates a connection between the plurality of calculationnodes, and generates the vehicle human machine interface based on to theconnection and the plurality of calculation nodes.
 17. The vehicle humanmachine interface generating system of claim 15, wherein the processormerges the plurality of predefined human machine interfaces by choosinga portion of the plurality of interface settings corresponding to themore than one occupant in the vehicle, and generates the vehicle humanmachine interface according to the chosen portion of the plurality ofinterface settings.
 18. The vehicle human machine interface generatingsystem of claim 15, wherein the processor interlocks the plurality ofpredefined human machine interfaces by determining a connection betweenthe plurality of interface settings corresponding to the more than oneoccupant in the vehicle, and generates the vehicle human machineinterface according to the connection between the plurality of interfacesettings.
 19. The vehicle human machine interface generating system ofclaim 15, wherein the processor determines a relationship between themore than one occupant in the vehicle, and generates the vehicle humanmachine interface according to relationship between the more than oneoccupants.
 20. A non-transitory computer-readable medium havinginstructions stored thereon that, when executed by at least oneprocessor, causes the at least one processor to perform a method forgenerating a vehicle human machine interface, comprising: determiningidentities of each of a plurality of occupants in a vehicle; obtaining aplurality of interface settings corresponding to the plurality ofoccupants in the vehicle according to the identities; and meshing theplurality of interface settings for display on the vehicle human machineinterface.